Advertisement

Coordination, Conventions and the Self-organisation of Sustainable Institutions

  • Jeremy Pitt
  • Julia Schaumeier
  • Alexander Artikis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7047)

Abstract

Applications where autonomous and heterogeneous agents form opportunistic alliances, which require them to share collective resources to achieve individual objectives, are increasingly common. We model such applications in terms of self-governing institutions for shared resource management. Socio-economic principles for enduring institutions are formalised in a logical framework for dynamic specification of norm-governed systems. The framework is implemented in an experimental testbed to investigate the interplay of coordination in a social dilemma with mutable conventions of an institution. Experimental results show that the presence of conventions enables the norm-governed system to approximate the performance of a theoretically ideal system. We conclude that this approach to self-organisation can provide the foundations for implementing sustainable electronic institutions.

Keywords

Cluster Member Cluster Average Social Dilemma Common Pool Resource Initial Compliance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ardagna, D., Panicucci, B., Passacantando, M.: A game theoretic formulation of the service provisioning problem in cloud systems. In: WWW 2011, pp. 177–186 (2011)Google Scholar
  2. 2.
    Artikis, A.: Dynamic protocols for open agent systems. In: Proc. AAMAS 2009, pp. 97–104. IFAAMAS (2009)Google Scholar
  3. 3.
    Artikis, A., Sergot, M., Pitt, J.: Specifying norm-governed computational societies. ACM Transactions on Computational Logic 10(1), 1–42 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Axtell, R.: Non-cooperative dynamics of multi-agent teams. In: Castelfranchi, C., Johnson, W. (eds.) Proceedings of the First International Joint Conference on Autonomous Agents and Multi-Agent Systems, pp. 1082–1089 (2002)Google Scholar
  5. 5.
    Axtell, R.: What economic agents do: How cognition and interaction lead to emergence and complexity. The Review of Austrian Economics 20(2), 105–122 (2007)CrossRefGoogle Scholar
  6. 6.
    Boulet, R., Mazzega, P., Jouve, B.: Environmental, social and normative networks in the maelia platform. In: Poblet, M., Schild, U., Zeleznikow, J. (eds.) Proc. ICAIL Worshop Legal & Decision Support Systems, pp. 83–93 (2009)Google Scholar
  7. 7.
    Gaechter, S.: Conditional cooperation: Behavioral regularities from the lab and the field and their policy implications. Discussion Papers 2006-03, The Centre for Decision Research and Experimental Economics, School of Economics, University of Nottingham (2006)Google Scholar
  8. 8.
    Janssen, M., Goldstone, R., Menczer, F., Ostrom, E.: Effect of rule choice in dynamic interactive spatial commons. International Journal of the Commons 2(2), 288–311 (2008)CrossRefGoogle Scholar
  9. 9.
    Jones, A., Pitt, J., Artikis, A.: On the analysis and implementation of normative systems: Towards a methodology: In: Cranefield, S., Noriega, P. (eds.) PreProceedings COIN@AAMAS 2011, pp. 47–56 (2011)Google Scholar
  10. 10.
    Jones, A., Sergot, M.: A formal characterisation of institutionalised power. Journal of the IGPL 4(3), 427–443 (1996)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Kowalski, R., Sergot, M.: A logic-based calculus of events. New Generation Computing 4, 67–95 (1986)CrossRefGoogle Scholar
  12. 12.
    Ostrom, E.: Governing the Commons. CUP (1990)Google Scholar
  13. 13.
    Pitt, J., Schaumeier, J., Artikis, A.: The axiomatisation of socio-economic principles for self-organising systems. In: Proceedings SASO 2011 (2011)Google Scholar
  14. 14.
    Raya, M., Hubaux, J.P.: Securing vehicular ad hoc networks. Journal of Computer Security 15(1), 39–68 (2007)CrossRefGoogle Scholar
  15. 15.
    Sandhu, R., Ferraiolo, D., Kuhn, R.: The NIST model for role-based access control: Toward a unified standard. In: 5th ACM Workshop Role-Based Access Control, RBAC 2000, pp. 47–63 (2000)Google Scholar
  16. 16.
    Strbac, G.: Demand side management: Benefits and challenges. Energy Policy 36(12), 4419–4426 (2008)CrossRefGoogle Scholar
  17. 17.
    Yamashita, T., Axtell, R., Kurumatani, K., Ohuchi, A.: Investigation of mutual choice metanorm in group dynamics for solving social dilemmas. In: Kurumatani, K., Chen, S.-H., Ohuchi, A. (eds.) IJCAI-WS 2003 and MAMUS 2003. LNCS (LNAI), vol. 3012, pp. 137–153. Springer, Heidelberg (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jeremy Pitt
    • 1
  • Julia Schaumeier
    • 1
  • Alexander Artikis
    • 1
    • 2
  1. 1.Department of Electrical & Electronic EngineeringImperial College LondonUK
  2. 2.National Centre for Scientific Research “Demokritos”AthensGreece

Personalised recommendations